In recent years there has been an exploding interest in extending the current applications
of multirotor UAVs to those that require aerial physical interaction, such as contact-based inspection, aerial writing, and tool handling on hard-to-reach surfaces.
Impedance control is a widely used interaction-control technique for aerial and ground robots. To achieve consistent performance during impedance control tasks, an a-priori knowledge of the environment parameters is needed to adjust the controller's impedance parameters accordingly. For the task of aerial writing on unknown surfaces, this unknown knowledge of the environment makes it challenging to achieve a consistent outcome for the interaction task.
In this MSc. assignment, a framework based on Bayesian-learning will be used to autonomously find the suitable parameters values of an impedance-controlled aerial robot. By requiring the drone to repeatedly perform a predetermined task in the environment, the learning agent would actively search in the impedance parameters space for the optimal controller gains and profile of the end-effector to perform a high-quality aerial writing task.
The work involves working with python, ROS and Gazebo for building a simulation environment to be used for learning. Experimental validation using in-house developed fully-actuated hexarotor is also possible.
For more reference:
https://ieeexplore.ieee.org/abstract/document/8848967